Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations664841
Missing cells570417
Missing cells (%)6.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory350.7 MiB
Average record size in memory553.1 B

Variable types

Text3
Categorical2
DateTime2
Numeric6

Alerts

end_lat is highly overall correlated with start_latHigh correlation
end_lng is highly overall correlated with start_lngHigh correlation
start_lat is highly overall correlated with end_latHigh correlation
start_lng is highly overall correlated with end_lngHigh correlation
start_station_name has 139752 (21.0%) missing values Missing
start_station_id has 139752 (21.0%) missing values Missing
end_station_name has 144920 (21.8%) missing values Missing
end_station_id has 145015 (21.8%) missing values Missing
end_lng is highly skewed (γ1 = 248.6115777) Skewed
ride_id has unique values Unique

Reproduction

Analysis started2025-05-04 18:31:44.792445
Analysis finished2025-05-04 18:32:01.403064
Duration16.61 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ride_id
Text

Unique 

Distinct664841
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size46.3 MiB
2025-05-04T13:32:01.711854image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters10637456
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique664841 ?
Unique (%)100.0%

Sample

1st row9C4EFBCCA63C94D8
2nd row302DB0DF65954B5A
3rd rowB8E500B89C4358DC
4th row05771DAB5118E255
5th rowA0BAFC8706AD756F
ValueCountFrequency (%)
9c4efbcca63c94d8 1
 
< 0.1%
f813364dde0ae483 1
 
< 0.1%
2de6b8d1a56a151c 1
 
< 0.1%
98f6b617e25798bf 1
 
< 0.1%
093fae32f60c1e45 1
 
< 0.1%
b8e500b89c4358dc 1
 
< 0.1%
05771dab5118e255 1
 
< 0.1%
a0bafc8706ad756f 1
 
< 0.1%
009bae9057af9068 1
 
< 0.1%
f2841b741a0f3d93 1
 
< 0.1%
Other values (664831) 664831
> 99.9%
2025-05-04T13:32:02.081538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 666241
 
6.3%
7 666133
 
6.3%
2 665876
 
6.3%
3 665582
 
6.3%
D 665277
 
6.3%
0 665219
 
6.3%
1 665090
 
6.3%
A 664611
 
6.2%
9 664492
 
6.2%
C 664491
 
6.2%
Other values (6) 3984444
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10637456
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 666241
 
6.3%
7 666133
 
6.3%
2 665876
 
6.3%
3 665582
 
6.3%
D 665277
 
6.3%
0 665219
 
6.3%
1 665090
 
6.3%
A 664611
 
6.2%
9 664492
 
6.2%
C 664491
 
6.2%
Other values (6) 3984444
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10637456
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 666241
 
6.3%
7 666133
 
6.3%
2 665876
 
6.3%
3 665582
 
6.3%
D 665277
 
6.3%
0 665219
 
6.3%
1 665090
 
6.3%
A 664611
 
6.2%
9 664492
 
6.2%
C 664491
 
6.2%
Other values (6) 3984444
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10637456
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 666241
 
6.3%
7 666133
 
6.3%
2 665876
 
6.3%
3 665582
 
6.3%
D 665277
 
6.3%
0 665219
 
6.3%
1 665090
 
6.3%
A 664611
 
6.2%
9 664492
 
6.2%
C 664491
 
6.2%
Other values (6) 3984444
37.5%

rideable_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 MiB
electric_bike
395735 
classic_bike
269106 

Length

Max length13
Median length13
Mean length12.595233
Min length12

Characters and Unicode

Total characters8373827
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowclassic_bike
2nd rowclassic_bike
3rd rowclassic_bike
4th rowelectric_bike
5th rowelectric_bike

Common Values

ValueCountFrequency (%)
electric_bike 395735
59.5%
classic_bike 269106
40.5%

Length

2025-05-04T13:32:02.173178image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-04T13:32:02.229886image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
electric_bike 395735
59.5%
classic_bike 269106
40.5%

Most occurring characters

ValueCountFrequency (%)
e 1456311
17.4%
c 1329682
15.9%
i 1329682
15.9%
l 664841
7.9%
_ 664841
7.9%
b 664841
7.9%
k 664841
7.9%
s 538212
 
6.4%
t 395735
 
4.7%
r 395735
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8373827
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1456311
17.4%
c 1329682
15.9%
i 1329682
15.9%
l 664841
7.9%
_ 664841
7.9%
b 664841
7.9%
k 664841
7.9%
s 538212
 
6.4%
t 395735
 
4.7%
r 395735
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8373827
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1456311
17.4%
c 1329682
15.9%
i 1329682
15.9%
l 664841
7.9%
_ 664841
7.9%
b 664841
7.9%
k 664841
7.9%
s 538212
 
6.4%
t 395735
 
4.7%
r 395735
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8373827
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1456311
17.4%
c 1329682
15.9%
i 1329682
15.9%
l 664841
7.9%
_ 664841
7.9%
b 664841
7.9%
k 664841
7.9%
s 538212
 
6.4%
t 395735
 
4.7%
r 395735
 
4.7%
Distinct664697
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size5.1 MiB
Minimum2024-08-31 00:14:28.111000
Maximum2024-09-30 23:56:49.220000
Invalid dates0
Invalid dates (%)0.0%
2025-05-04T13:32:02.295372image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:32:02.373061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct664648
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size5.1 MiB
Minimum2024-09-01 00:00:14.521000
Maximum2024-09-30 23:59:54.421000
Invalid dates0
Invalid dates (%)0.0%
2025-05-04T13:32:02.445741image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:32:02.519906image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

start_station_name
Text

Missing 

Distinct779
Distinct (%)0.1%
Missing139752
Missing (%)21.0%
Memory size44.4 MiB
2025-05-04T13:32:02.656108image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length61
Median length50
Mean length23.071792
Min length10

Characters and Unicode

Total characters12114744
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSaint Asaph St & Madison St
2nd row17th & K St NW / Farragut Square
3rd row9th & N St NW
4th rowEisenhower Ave & Mill Race Ln
5th rowEisenhower Ave & Mill Race Ln
ValueCountFrequency (%)
548005
18.1%
st 432098
 
14.3%
nw 293651
 
9.7%
ave 158925
 
5.2%
ne 66786
 
2.2%
14th 38253
 
1.3%
se 32824
 
1.1%
n 31711
 
1.0%
new 31300
 
1.0%
rd 29707
 
1.0%
Other values (800) 1364236
45.1%
2025-05-04T13:32:02.893658image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2510833
20.7%
t 1055229
 
8.7%
e 624914
 
5.2%
S 544891
 
4.5%
& 484396
 
4.0%
n 463794
 
3.8%
a 453678
 
3.7%
N 450034
 
3.7%
r 417642
 
3.4%
o 410157
 
3.4%
Other values (60) 4699176
38.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12114744
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2510833
20.7%
t 1055229
 
8.7%
e 624914
 
5.2%
S 544891
 
4.5%
& 484396
 
4.0%
n 463794
 
3.8%
a 453678
 
3.7%
N 450034
 
3.7%
r 417642
 
3.4%
o 410157
 
3.4%
Other values (60) 4699176
38.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12114744
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2510833
20.7%
t 1055229
 
8.7%
e 624914
 
5.2%
S 544891
 
4.5%
& 484396
 
4.0%
n 463794
 
3.8%
a 453678
 
3.7%
N 450034
 
3.7%
r 417642
 
3.4%
o 410157
 
3.4%
Other values (60) 4699176
38.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12114744
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2510833
20.7%
t 1055229
 
8.7%
e 624914
 
5.2%
S 544891
 
4.5%
& 484396
 
4.0%
n 463794
 
3.8%
a 453678
 
3.7%
N 450034
 
3.7%
r 417642
 
3.4%
o 410157
 
3.4%
Other values (60) 4699176
38.8%

start_station_id
Real number (ℝ)

Missing 

Distinct777
Distinct (%)0.1%
Missing139752
Missing (%)21.0%
Infinite0
Infinite (%)0.0%
Mean31380.29
Minimum30200
Maximum33200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 MiB
2025-05-04T13:32:02.989513image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum30200
5-th percentile31056
Q131209
median31289
Q331609
95-th percentile31929
Maximum33200
Range3000
Interquartile range (IQR)400

Descriptive statistics

Standard deviation287.86898
Coefficient of variation (CV)0.0091735602
Kurtosis4.085314
Mean31380.29
Median Absolute Deviation (MAD)170
Skewness0.85627807
Sum1.6477445 × 1010
Variance82868.547
MonotonicityNot monotonic
2025-05-04T13:32:03.064580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31623 5467
 
0.8%
31229 5075
 
0.8%
31201 4546
 
0.7%
31600 4482
 
0.7%
31603 4439
 
0.7%
31101 4233
 
0.6%
31200 3703
 
0.6%
31627 3664
 
0.6%
31258 3605
 
0.5%
31236 3542
 
0.5%
Other values (767) 482333
72.5%
(Missing) 139752
 
21.0%
ValueCountFrequency (%)
30200 1366
0.2%
30201 1670
0.3%
31000 583
 
0.1%
31002 710
0.1%
31003 585
 
0.1%
31004 253
 
< 0.1%
31005 836
0.1%
31006 724
0.1%
31007 780
0.1%
31009 250
 
< 0.1%
ValueCountFrequency (%)
33200 688
0.1%
32901 27
 
< 0.1%
32609 42
 
< 0.1%
32608 59
 
< 0.1%
32607 45
 
< 0.1%
32606 153
 
< 0.1%
32605 83
 
< 0.1%
32604 59
 
< 0.1%
32603 48
 
< 0.1%
32602 40
 
< 0.1%

end_station_name
Text

Missing 

Distinct781
Distinct (%)0.2%
Missing144920
Missing (%)21.8%
Memory size44.1 MiB
2025-05-04T13:32:03.194802image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length61
Median length51
Mean length23.021399
Min length10

Characters and Unicode

Total characters11969309
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMarket Square / King St & Royal St
2nd row15th & W St NW
3rd rowRhode Island Ave & 39th St / Brentwood Arts Exchange
4th rowBallenger Ave & Dulaney St
5th rowBallenger Ave & Dulaney St
ValueCountFrequency (%)
544009
18.1%
st 431537
 
14.4%
nw 289918
 
9.7%
ave 158796
 
5.3%
ne 66782
 
2.2%
14th 36576
 
1.2%
se 33543
 
1.1%
new 31631
 
1.1%
sw 30171
 
1.0%
m 30001
 
1.0%
Other values (802) 1349207
44.9%
2025-05-04T13:32:03.429676image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2490514
20.8%
t 1048159
 
8.8%
e 617753
 
5.2%
S 545507
 
4.6%
& 480613
 
4.0%
n 457158
 
3.8%
a 446153
 
3.7%
N 444714
 
3.7%
r 410484
 
3.4%
o 400604
 
3.3%
Other values (60) 4627650
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11969309
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2490514
20.8%
t 1048159
 
8.8%
e 617753
 
5.2%
S 545507
 
4.6%
& 480613
 
4.0%
n 457158
 
3.8%
a 446153
 
3.7%
N 444714
 
3.7%
r 410484
 
3.4%
o 400604
 
3.3%
Other values (60) 4627650
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11969309
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2490514
20.8%
t 1048159
 
8.8%
e 617753
 
5.2%
S 545507
 
4.6%
& 480613
 
4.0%
n 457158
 
3.8%
a 446153
 
3.7%
N 444714
 
3.7%
r 410484
 
3.4%
o 400604
 
3.3%
Other values (60) 4627650
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11969309
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2490514
20.8%
t 1048159
 
8.8%
e 617753
 
5.2%
S 545507
 
4.6%
& 480613
 
4.0%
n 457158
 
3.8%
a 446153
 
3.7%
N 444714
 
3.7%
r 410484
 
3.4%
o 400604
 
3.3%
Other values (60) 4627650
38.7%

end_station_id
Real number (ℝ)

Missing 

Distinct778
Distinct (%)0.1%
Missing145015
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean31381.767
Minimum30200
Maximum33200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 MiB
2025-05-04T13:32:03.521874image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum30200
5-th percentile31060
Q131212
median31289
Q331611
95-th percentile31928
Maximum33200
Range3000
Interquartile range (IQR)399

Descriptive statistics

Standard deviation288.86454
Coefficient of variation (CV)0.0092048525
Kurtosis4.2357769
Mean31381.767
Median Absolute Deviation (MAD)170
Skewness0.81349061
Sum1.6313059 × 1010
Variance83442.722
MonotonicityNot monotonic
2025-05-04T13:32:03.597336image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31623 5439
 
0.8%
31229 4998
 
0.8%
31201 4557
 
0.7%
31600 4556
 
0.7%
31603 4460
 
0.7%
31101 4169
 
0.6%
31627 3723
 
0.6%
31200 3720
 
0.6%
31236 3582
 
0.5%
31613 3429
 
0.5%
Other values (768) 477193
71.8%
(Missing) 145015
 
21.8%
ValueCountFrequency (%)
30200 1609
0.2%
30201 1709
0.3%
31000 608
 
0.1%
31002 712
0.1%
31003 582
 
0.1%
31004 253
 
< 0.1%
31005 830
0.1%
31006 732
0.1%
31007 771
0.1%
31009 256
 
< 0.1%
ValueCountFrequency (%)
33200 702
0.1%
32901 80
 
< 0.1%
32609 38
 
< 0.1%
32608 67
 
< 0.1%
32607 52
 
< 0.1%
32606 129
 
< 0.1%
32605 80
 
< 0.1%
32604 62
 
< 0.1%
32603 58
 
< 0.1%
32602 44
 
< 0.1%

start_lat
Real number (ℝ)

High correlation 

Distinct34881
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.906499
Minimum38.74
Maximum39.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 MiB
2025-05-04T13:32:03.672008image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum38.74
5-th percentile38.862841
Q138.89245
median38.905578
Q338.92
95-th percentile38.953691
Maximum39.13
Range0.39
Interquartile range (IQR)0.027549906

Descriptive statistics

Standard deviation0.029197204
Coefficient of variation (CV)0.00075044543
Kurtosis5.7108485
Mean38.906499
Median Absolute Deviation (MAD)0.0144215
Skewness0.38571918
Sum25866636
Variance0.00085247674
MonotonicityNot monotonic
2025-05-04T13:32:03.753505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.9 20983
 
3.2%
38.91 20433
 
3.1%
38.92 18416
 
2.8%
38.93 15592
 
2.3%
38.88 9324
 
1.4%
38.94 8642
 
1.3%
38.89 8151
 
1.2%
38.95 7990
 
1.2%
38.96 7254
 
1.1%
38.89696 5223
 
0.8%
Other values (34871) 542833
81.6%
ValueCountFrequency (%)
38.74 13
 
< 0.1%
38.76 29
< 0.1%
38.76552765 1
 
< 0.1%
38.766844 26
 
< 0.1%
38.76687503 1
 
< 0.1%
38.76687562 1
 
< 0.1%
38.76688898 1
 
< 0.1%
38.76691317 1
 
< 0.1%
38.766946 65
< 0.1%
38.76843 15
 
< 0.1%
ValueCountFrequency (%)
39.13 17
 
< 0.1%
39.12582811 26
< 0.1%
39.123513 14
 
< 0.1%
39.12333798 1
 
< 0.1%
39.12333 23
 
< 0.1%
39.121327 35
< 0.1%
39.12124586 1
 
< 0.1%
39.121104 1
 
< 0.1%
39.12 60
< 0.1%
39.11988258 1
 
< 0.1%

start_lng
Real number (ℝ)

High correlation 

Distinct35211
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-77.03277
Minimum-77.39
Maximum-76.825416
Zeros0
Zeros (%)0.0%
Negative664841
Negative (%)100.0%
Memory size5.1 MiB
2025-05-04T13:32:03.888764image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-77.39
5-th percentile-77.091862
Q1-77.045
median-77.03
Q3-77.011616
95-th percentile-76.987823
Maximum-76.825416
Range0.56458391
Interquartile range (IQR)0.033384

Descriptive statistics

Standard deviation0.037667961
Coefficient of variation (CV)-0.00048898619
Kurtosis16.606255
Mean-77.03277
Median Absolute Deviation (MAD)0.016912611
Skewness-2.4745639
Sum-51214544
Variance0.0014188753
MonotonicityNot monotonic
2025-05-04T13:32:03.971436image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-77.03 28869
 
4.3%
-77.02 18417
 
2.8%
-77.04 16154
 
2.4%
-77 12167
 
1.8%
-77.01 10959
 
1.6%
-77.05 6794
 
1.0%
-76.99 5922
 
0.9%
-77.07 5296
 
0.8%
-77.00493 5223
 
0.8%
-77.06 4989
 
0.8%
Other values (35201) 550051
82.7%
ValueCountFrequency (%)
-77.39 2
 
< 0.1%
-77.38 3
 
< 0.1%
-77.37 43
< 0.1%
-77.368416 50
< 0.1%
-77.36837947 1
 
< 0.1%
-77.36800253 1
 
< 0.1%
-77.36786842 5
 
< 0.1%
-77.36663961 1
 
< 0.1%
-77.366499 29
< 0.1%
-77.3662945 1
 
< 0.1%
ValueCountFrequency (%)
-76.82541609 1
 
< 0.1%
-76.825535 21
< 0.1%
-76.83 18
 
< 0.1%
-76.8387907 1
 
< 0.1%
-76.84 29
< 0.1%
-76.843263 18
 
< 0.1%
-76.84326744 1
 
< 0.1%
-76.84326827 1
 
< 0.1%
-76.844604 49
< 0.1%
-76.85 6
 
< 0.1%

end_lat
Real number (ℝ)

High correlation 

Distinct834
Distinct (%)0.1%
Missing489
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean38.905888
Minimum35.84
Maximum42.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 MiB
2025-05-04T13:32:04.054617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum35.84
5-th percentile38.862398
Q138.892275
median38.905303
Q338.919018
95-th percentile38.952369
Maximum42.99
Range7.15
Interquartile range (IQR)0.0267435

Descriptive statistics

Standard deviation0.030324128
Coefficient of variation (CV)0.00077942258
Kurtosis953.01831
Mean38.905888
Median Absolute Deviation (MAD)0.013506
Skewness2.9438704
Sum25847204
Variance0.00091955271
MonotonicityNot monotonic
2025-05-04T13:32:04.142180image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.9 21403
 
3.2%
38.91 21049
 
3.2%
38.92 19028
 
2.9%
38.93 16127
 
2.4%
38.88 9561
 
1.4%
38.94 8856
 
1.3%
38.89 8562
 
1.3%
38.95 8189
 
1.2%
38.96 7456
 
1.1%
38.89696 5439
 
0.8%
Other values (824) 538682
81.0%
ValueCountFrequency (%)
35.84 1
 
< 0.1%
36.07 1
 
< 0.1%
38.63 1
 
< 0.1%
38.67 1
 
< 0.1%
38.71 7
 
< 0.1%
38.72 3
 
< 0.1%
38.73 2
 
< 0.1%
38.74 19
< 0.1%
38.75 3
 
< 0.1%
38.76 25
< 0.1%
ValueCountFrequency (%)
42.99 1
 
< 0.1%
42.08 1
 
< 0.1%
39.28 1
 
< 0.1%
39.25 1
 
< 0.1%
39.13 16
 
< 0.1%
39.12582811 25
< 0.1%
39.123513 11
 
< 0.1%
39.12333 19
 
< 0.1%
39.121327 34
< 0.1%
39.12 61
< 0.1%

end_lng
Real number (ℝ)

High correlation  Skewed 

Distinct855
Distinct (%)0.1%
Missing489
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-77.03249
Minimum-80.18
Maximum-48.7
Zeros0
Zeros (%)0.0%
Negative664352
Negative (%)99.9%
Memory size5.1 MiB
2025-05-04T13:32:04.220850image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-80.18
5-th percentile-77.09
Q1-77.045
median-77.03
Q3-77.010266
95-th percentile-76.987633
Maximum-48.7
Range31.48
Interquartile range (IQR)0.034734

Descriptive statistics

Standard deviation0.051552333
Coefficient of variation (CV)-0.00066922844
Kurtosis137357.31
Mean-77.03249
Median Absolute Deviation (MAD)0.017317624
Skewness248.61158
Sum-51176688
Variance0.002657643
MonotonicityNot monotonic
2025-05-04T13:32:04.299915image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-77.03 29865
 
4.5%
-77.02 18919
 
2.8%
-77.04 16687
 
2.5%
-77 12484
 
1.9%
-77.01 11333
 
1.7%
-77.05 7016
 
1.1%
-76.99 6209
 
0.9%
-77.07 5455
 
0.8%
-77.00493 5439
 
0.8%
-77.06 5059
 
0.8%
Other values (845) 545886
82.1%
ValueCountFrequency (%)
-80.18 1
 
< 0.1%
-78.69 1
 
< 0.1%
-78.29 1
 
< 0.1%
-77.68 1
 
< 0.1%
-77.49 1
 
< 0.1%
-77.4 1
 
< 0.1%
-77.39 3
 
< 0.1%
-77.38 2
 
< 0.1%
-77.37 44
< 0.1%
-77.368416 53
< 0.1%
ValueCountFrequency (%)
-48.7 1
 
< 0.1%
-74.75 1
 
< 0.1%
-76.51 1
 
< 0.1%
-76.74 1
 
< 0.1%
-76.76 1
 
< 0.1%
-76.78 1
 
< 0.1%
-76.825535 19
< 0.1%
-76.83 25
< 0.1%
-76.84 34
< 0.1%
-76.843263 19
< 0.1%

member_casual
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.9 MiB
member
435233 
casual
229608 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters3989046
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmember
2nd rowmember
3rd rowcasual
4th rowmember
5th rowmember

Common Values

ValueCountFrequency (%)
member 435233
65.5%
casual 229608
34.5%

Length

2025-05-04T13:32:04.373604image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-04T13:32:04.427602image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
member 435233
65.5%
casual 229608
34.5%

Most occurring characters

ValueCountFrequency (%)
m 870466
21.8%
e 870466
21.8%
a 459216
11.5%
b 435233
10.9%
r 435233
10.9%
c 229608
 
5.8%
s 229608
 
5.8%
u 229608
 
5.8%
l 229608
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3989046
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 870466
21.8%
e 870466
21.8%
a 459216
11.5%
b 435233
10.9%
r 435233
10.9%
c 229608
 
5.8%
s 229608
 
5.8%
u 229608
 
5.8%
l 229608
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3989046
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 870466
21.8%
e 870466
21.8%
a 459216
11.5%
b 435233
10.9%
r 435233
10.9%
c 229608
 
5.8%
s 229608
 
5.8%
u 229608
 
5.8%
l 229608
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3989046
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 870466
21.8%
e 870466
21.8%
a 459216
11.5%
b 435233
10.9%
r 435233
10.9%
c 229608
 
5.8%
s 229608
 
5.8%
u 229608
 
5.8%
l 229608
 
5.8%

Interactions

2025-05-04T13:31:58.714805image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:55.966105image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:56.547913image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:57.043931image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:57.616289image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:58.159472image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:58.797257image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:56.092237image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:56.627478image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:57.144945image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:57.699684image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:58.243143image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:58.887804image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:56.201256image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:56.706055image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:57.236400image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:57.789216image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:58.331247image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:58.981292image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:56.299070image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:56.791630image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:57.329046image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:57.879481image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:58.423682image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:59.074571image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:56.384481image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:56.874538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:57.423721image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:57.980894image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:58.511079image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:59.160671image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:56.469963image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:56.961131image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:57.522162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:58.080035image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-04T13:31:58.626498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-05-04T13:32:04.471212image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
end_latend_lngend_station_idmember_casualrideable_typestart_latstart_lngstart_station_id
end_lat1.000-0.018-0.1280.0000.0000.7360.007-0.057
end_lng-0.0181.0000.4530.0410.0440.0000.7320.318
end_station_id-0.1280.4531.0000.0870.046-0.0680.3120.369
member_casual0.0000.0410.0871.0000.0150.1060.0590.091
rideable_type0.0000.0440.0460.0151.0000.1860.0750.053
start_lat0.7360.000-0.0680.1060.1861.000-0.019-0.115
start_lng0.0070.7320.3120.0590.075-0.0191.0000.452
start_station_id-0.0570.3180.3690.0910.053-0.1150.4521.000

Missing values

2025-05-04T13:31:59.320569image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-04T13:31:59.892755image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-04T13:32:00.929308image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ride_idrideable_typestarted_atended_atstart_station_namestart_station_idend_station_nameend_station_idstart_latstart_lngend_latend_lngmember_casual
09C4EFBCCA63C94D8classic_bike2024-09-03 14:02:18.9852024-09-03 14:06:30.572Saint Asaph St & Madison St31097.0Market Square / King St & Royal St31042.038.812718-77.04409738.804718-77.043363member
1302DB0DF65954B5Aclassic_bike2024-09-27 17:12:06.8992024-09-27 17:25:46.53017th & K St NW / Farragut Square31233.015th & W St NW31125.038.902061-77.03832238.919018-77.034449member
2B8E500B89C4358DCclassic_bike2024-09-27 18:05:43.8712024-09-27 18:43:56.0519th & N St NW31336.0Rhode Island Ave & 39th St / Brentwood Arts Exchange32413.038.906622-77.02388538.939271-76.955047casual
305771DAB5118E255electric_bike2024-09-13 14:57:35.4172024-09-13 15:00:11.060Eisenhower Ave & Mill Race Ln31082.0Ballenger Ave & Dulaney St31084.038.801111-77.06895238.802677-77.063562member
4A0BAFC8706AD756Felectric_bike2024-09-13 09:04:49.2662024-09-13 09:06:55.120Eisenhower Ave & Mill Race Ln31082.0Ballenger Ave & Dulaney St31084.038.801111-77.06895238.802677-77.063562member
5009BAE9057AF9068electric_bike2024-09-29 17:17:52.2812024-09-29 17:26:10.3952nd St & Massachusetts Ave NE31641.013th & E St SE31607.038.894972-77.00313538.882915-76.987907member
6F2841B741A0F3D93classic_bike2024-09-26 08:57:59.1042024-09-26 09:01:16.587Eisenhower Ave & Mill Race Ln31082.0Ballenger Ave & Dulaney St31084.038.801111-77.06895238.802677-77.063562member
71A4241FFFA6EEEE6electric_bike2024-09-11 08:54:26.8652024-09-11 08:56:20.829Eisenhower Ave & Mill Race Ln31082.0Ballenger Ave & Dulaney St31084.038.801111-77.06895238.802677-77.063562member
8A925A49B6E431C74classic_bike2024-09-22 17:29:37.4062024-09-22 17:35:46.4031st & K St NE31662.05th & K St NW31600.038.902386-77.00564938.902734-77.019181member
9093FAE32F60C1E45electric_bike2024-09-23 18:18:20.2332024-09-23 18:24:03.605Friendship Hts Metro / Wisconsin Ave & Wisconsin Cir32014.0Connecticut Ave & McKinley St NW31315.038.961763-77.08599838.964544-77.075135member
ride_idrideable_typestarted_atended_atstart_station_namestart_station_idend_station_nameend_station_idstart_latstart_lngend_latend_lngmember_casual
66483173B124E9F7532452electric_bike2024-09-15 20:34:16.4142024-09-15 20:46:22.981NaNNaNNaNNaN38.85-76.9738.85-76.98member
664832E0732775F6664738electric_bike2024-09-15 22:14:16.1042024-09-15 22:29:38.215NaNNaNNaNNaN38.85-76.9838.88-76.97member
664833B4C52DDB80BC45C7electric_bike2024-09-15 23:51:16.7422024-09-16 00:00:21.009NaNNaNNaNNaN38.88-76.9338.88-76.93member
664834B87AC79F0732D076electric_bike2024-09-16 19:56:24.5292024-09-16 20:48:01.618NaNNaNNaNNaN38.85-76.9838.85-76.98member
664835F7FB65FE36ABFDC6electric_bike2024-09-16 10:53:53.2802024-09-16 11:25:21.797NaNNaNNaNNaN38.91-76.9338.85-76.98member
6648364C2859F401A89BE4electric_bike2024-09-16 17:23:10.5532024-09-16 18:15:01.087NaNNaNNaNNaN38.88-77.0038.90-76.92member
66483736C4ADE33349BF44electric_bike2024-09-16 08:59:39.2572024-09-16 09:18:16.610NaNNaNNaNNaN38.88-76.9338.85-76.98member
664838F01550D1A53887E1electric_bike2024-09-16 06:42:03.1532024-09-16 06:58:53.820NaNNaNNaNNaN38.93-77.0438.90-77.01member
6648395D3E7F1E248D4781electric_bike2024-09-16 14:56:28.2512024-09-16 14:59:51.050NaNNaNNaNNaN38.93-77.0438.94-77.04member
66484043524A3F14B4411Delectric_bike2024-09-16 15:03:28.7172024-09-16 15:08:38.661NaNNaNNaNNaN38.94-77.0438.93-77.03member